A spatiotemporally ambiguous event (SAE) is characterized by data that suffers from significant location and/or time measurement inaccuracies. For example, location data collected by devices such as cell phones may be spatiotemporally ambiguous because many cell phones only associate a given location with a street address rather than providing more precise geo-location information. One conventional analysis technique for dealing with such spatiotemporally ambiguous data is to simply make the scale of the modeling coarse enough to allow the ambiguity to become negligible.